Work

ThermoPred: AI-Enhanced Quantum Chemistry Data Set and ML Toolkit for Thermochemical Properties of API-Like Compounds and Their Degradants

Year: 2025

Type: article

Abstract: High Resolution Image Download MS PowerPoint Slide In this work, we present an open-access quantum-chemistry database of more than 14,500 API-like molecules and their degradation products, all optimized at the M06-2 X /6-31G(d) compound model. The data set delivers a comprehensive suite of thermochemical and quantum descriptors─including Gibbs free energy, enthalpy, electronic energy, vibrational frequencies and Cartesian geometries─tailored for large-scale modeling. Leveraging these data, we trained and validated three machine-learning models (XGBoost, Random Forest and Multi-Layer Perceptron) to enable rapid, accurate prediction of Gibbs free energy and enthalpy. These models are bundled in ThermoPred, an open-source Python package that offers a scalable, computationally efficient alternative to traditional quantum-chemical calculations. All data sets, models and source code are freely available to support reproducibility and foster community-driven development.

Source: Journal of Chemical Information and Modeling

DOI: 10.1021/acs.jcim.5c01320

Authors: Diullio P. Santos, Jefferson R. Dias-Silva, Luiz H. K. Q. Júnior, Heibbe C. B. de Oliveira

Institution: Universidade Federal de Goiás

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,500

Funders: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado de Goiás